220266-en_US
About the role
The Solution Architect – Rack Integration & DC Compute, AI Data Center is a customer-facing technical role responsible for architecting and operationalizing rack-level compute infrastructure for large-scale AI data center deployments. The role connects server, GPU, storage, networking, power, cooling, rack design, factory integration, and onsite deployment requirements into a complete, executable solution. The ideal candidate combines deep compute and data center infrastructure expertise with hands-on knowledge of rack integration and manufacturing processes. The role works closely with customers, Solution Specialists, OEMs, engineering, Professional Services, integration centers, supply chain, project management, and field delivery teams.
What you'll be doing
- Lead technical discovery and solution architecture for AI compute and rack integration opportunities.
- Translate customer GPU, server, storage, network, power, cooling, and deployment requirements into standardized rack-level designs.
- Develop and validate rack architectures, rack elevations, BOMs, component placement, power requirements, network connectivity, and integration specifications.
- Architect solutions for GPU and accelerated compute platforms, high-density AI racks, traditional compute platforms, storage, and supporting infrastructure.
- Define rack integration workflows including receiving, kitting, assembly, installation, cabling, configuration, firmware, burn-in, testing, QA/QC, packaging, and shipment.
- Partner with compute, networking, storage, power, cooling, rack, and integration OEMs to validate compatibility and technical requirements.
- Support high-density and liquid-cooled rack designs, including CDU interfaces, manifolds, power distribution, weight, space, and facility dependencies.
- Develop repeatable rack build standards and golden configurations that can be scaled across large AI Data Center programs.
- Support RFQs/RFPs, proposals, statements of work, technical presentations, customer workshops, and design reviews.
- Work with integration centers and field delivery teams to ensure designs are manufacturable, deployable, testable, and supportable at scale.
- Define acceptance criteria, validation plans, documentation standards, and design-to-delivery handoff requirements.
- Identify opportunities to automate BOM creation, rack design, configuration, validation, testing, and deployment documentation.
What you have
- 7+ years of experience in data center compute, converged infrastructure, systems engineering, solution architecture, rack integration, or related infrastructure roles.
- Strong background in one or more AI Data Center areas including GPU/accelerated compute, server platforms, converged or hyperconverged infrastructure, rack integration, high-density compute, AI/HPC infrastructure, storage, or data center networking.
- Deep understanding of enterprise and data center compute platforms, including server architecture, CPU/GPU systems, memory, storage, PCIe, NICs/DPUs, firmware, and systems management.
- Experience designing or supporting integrated rack solutions including compute, network, storage, PDUs, cabling, power, cooling, and mechanical requirements.
- Experience with large-scale rack build, factory integration, staging, configuration, test, QA/QC, and deployment processes is strongly preferred.
- Experience with AI CSPs, hyperscalers, NCPs, HPC environments, large data center operators, or major compute OEMs is highly desirable.
- Understanding of high-density power and liquid cooling requirements associated with current and next-generation AI compute platforms.
- Ability to create and validate rack elevations, BOMs, build documentation, configuration standards, test plans, and implementation requirements.
- Experience working with major compute and infrastructure OEMs and understanding technology roadmaps and lifecycle considerations.
- Strong troubleshooting and root-cause analysis skills across integrated compute, network, storage, firmware, and physical infrastructure.
- Strong customer-facing communication, technical presentation, documentation, and architecture-review skills.
- Ability to work across sales, engineering, OEMs, integration operations, Professional Services, supply chain, project management, and field delivery.
- NVIDIA GPU and accelerated computing platforms, including integrated AI rack architectures
- Dell Technologies, HPE, Lenovo, Supermicro, or other enterprise/AI compute platforms
- Converged and hyperconverged infrastructure
- AI/HPC clusters and large-scale GPU deployments
- High-density rack integration and factory build environments
- Liquid-cooled compute and rack-level thermal infrastructure
- 400G/800G Ethernet and InfiniBand connectivity within AI compute environments
- Enterprise and AI storage platforms
- Firmware, BIOS, BMC, systems management, configuration, and validation
- Burn-in, functional testing, L1/L2 validation, QA/QC, and rack acceptance testing
- Automation of rack design, BOMs, configuration, testing, and deployment workflows
- Multi-site deployment standards and repeatable AI infrastructure methodologies
- Compute Architecture – Deep understanding of server, GPU, storage, networking, and integrated data center compute platforms.
- Rack Integration – Understands how to convert a technical architecture into a repeatable physical rack build and integration process.
- AI Infrastructure Knowledge – Understands the density, performance, power, cooling, connectivity, and operational requirements of AI compute environments.
- Design for Scale – Creates standardized architectures and golden configurations that can be replicated across hundreds or thousands of racks.
- Technical Problem Solving – Can troubleshoot complex issues across hardware, firmware, networking, storage, power, cooling, and physical integration.
- Design-to-Delivery Thinking – Architects solutions with manufacturing, logistics, deployment, testing, field installation, and lifecycle support in mind.
- Customer Engagement – Leads technical discovery, architecture workshops, solution reviews, and executive-level technical discussions.
- Cross-Functional Leadership – Connects customer requirements with OEMs, engineering, integration centers, sourcing, Professional Services, and field delivery.
- Quality, accuracy, and scalability of AI compute and rack integration designs
- Speed from customer requirements to an executable rack architecture and BOM
- Standardization and repeatability of rack designs and integration processes
- Reduction in integration defects, field changes, rework, and deployment issues
- Successful technical support of AI Data Center pursuits and customer programs
- Accuracy of BOMs, rack elevations, build documentation, and configuration standards
- Successful transition from solution architecture through integration and onsite deployment
- Customer and OEM confidence in the technical solution
- Continuous improvement and automation of rack integration methodology
The ideal candidate is a highly technical data center compute architect who understands both the logical architecture of AI infrastructure and the physical realities of building it at rack scale. They should be able to work from a customer's compute and GPU requirements down through server configuration, rack layout, networking, power, cooling, firmware, cabling, integration, testing, and deployment. A profile similar to the current role combines AI solution architecture with deep prior experience in compute, converged/hyperconverged infrastructure, engineering, and hands-on infrastructure design. The strongest candidates will be builders who can turn complex AI platform requirements into standardized, factory-integrated solutions that can be deployed rapidly and consistently at scale.
